Mastering Machine Learning Algorithms 2025

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Files
  1. Get Bonus Downloads Here.url180 bytes
  2. 1 -Introduction to the Course.mp410.5 MB
  3. 2 -What is Machine Learning with Example.mp490.5 MB
  4. 3 -Tom M. Mitchell Definition of Machine Learning.mp423.5 MB
  5. 4 -Types of Machine Learning and List of most ML algorithms.mp455.6 MB
  6. 5 - Read and Learn - List of All Machine Learning Algorithms.html4.5 KB
  7. 5 - Read and Learn - What are the types of Machine Learning.html2.6 KB
  8. 5 - Read and Learn - What is Machine Learning and its applications.html3.8 KB
  9. 1 -Hold Out Cross Validation Technique.mp423.3 MB
  10. 10 -Parameters and Hyper-Parameters of the ML Algorithms.mp449.9 MB
  11. 11 -GridSearchCV - Hyper-Parameter Tuning Method.mp444.8 MB
  12. 2 -K-Fold Cross Validation Technique.mp426.4 MB
  13. 3 -Stratified K-Fold Cross Validation Technique.mp466.2 MB
  14. 4 -Leave P-Out Cross Validation Technique.mp431.8 MB
  15. 5 -Leave One Out Cross Validation.mp410.4 MB
  16. 6 -Imbalanced Dataset.mp426.3 MB
  17. 7 -OverSampling and UnderSampling.mp425.4 MB
  18. 8 -Synthetic Minority Oversampling Technique (SMOTE).mp418.6 MB
  19. 9 -Use case using the SMOTE.mp437.4 MB
  20. 1 -Introduction to Correlation and Regression.mp457.3 MB
  21. 2 - Read and Learn - What is Correlation and Regression.html2.8 KB
  22. 2 -Regression Algorithm Assumptions.mp452.3 MB
  23. 3 - Read and Learn - Linear Regression algorithm Assumptions.html3.1 KB
  24. 3 -Simple and Multi Linear Regression (SLR) Algorithm.mp486.4 MB
  25. 4 - Read and Learn - Multi Linear Regression with Implementation Example.html3.3 KB
  26. 4 - Read and Learn - Simple Linear Regression with Implementation Example.html2.0 KB
  27. 4 -Hypothesis Testing to evaluate the significance of regression line.mp441.8 MB
  28. 5 -R-Square Performance Measure.mp445.8 MB
  29. 6 -Simple Linear Regression Implementation using sklearn library.mp418.2 MB
  30. 7 -Introduction to Use Case.mp419.5 MB
  31. 8 -Use case discussion.mp473.9 MB
  32. 1 -What is classification and regression.mp419.6 MB
  33. 10 -Maximum Likelihood Estimation (MLE).mp477.6 MB
  34. 11 -Solving Logistic Regression Example with MLE.mp423.2 MB
  35. 2 -What is Logistic Regression, How it is different from linear regression and how.mp447.0 MB
  36. 3 -Logistic Regression Explanation with Example.mp447.2 MB
  37. 4 -Linear VS Logistic Regression.mp447.3 MB
  38. 5 -Confusion Matrix.mp460.9 MB
  39. 6 -Performance Metrics in Classification.mp444.9 MB
  40. 7 -Difference between Probability and Odds.mp471.5 MB
  41. 8 -Logistic Regression Derivation.mp421.1 MB
  42. 9 -Difference between Probability and Likelihood.mp432.6 MB
  43. 1 -Agenda.mp46.9 MB
  44. 2 -What is DT, its intuition and Terminologies.mp498.6 MB
  45. 3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4125.3 MB
  46. 4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4129.4 MB
  47. 5 -CART Decision Tree Algorithm - wrt Classification.mp447.7 MB
  48. 6 -CART Decision Tree Algorithm - wrt Regression.mp437.4 MB
  49. 7 - Implementation of CART using SKLearn Library.html5.6 KB
  50. 7 -Use case on Decision Tree - Prediction of Wine Quality.mp481.1 MB
  51. 1 -Parametric and Non-Parametric ML Algorithms.mp451.3 MB
  52. 2 -Distance Measures.mp450.8 MB
  53. 3 -Introduction to KNN Algorithm.mp470.0 MB
  54. 4 -How KNN Algorithm works.mp418.5 MB
  55. 5 -How to find optimum K Value in KNN.mp432.1 MB
  56. 6 -Use case explaining KNN implementation.mp424.7 MB
  57. 7 -Example - How to find an optimum k value for KNN.mp426.8 MB
  58. 1 -Partition Theorem.mp426.4 MB
  59. 2 -Naïve Bayes Algorithm Pre-requisites.mp453.3 MB
  60. 3 -Bayes Theorem With Example.mp459.2 MB
  61. 4 -Bayes Theorem Formal Defination.mp412.9 MB
  62. 5 -Naïve Bayes Classifier with example.mp466.1 MB
  63. 1 -Recap of our learning.mp411.6 MB
  64. 10 -Elbow Method.mp423.4 MB
  65. 11 -Performance Metrics in Clustering.mp423.7 MB
  66. 12 -Silhouette Score Example.mp425.4 MB
  67. 13 -Use case using Silhouette score.mp428.4 MB
  68. 2 -Agenda.mp46.8 MB
  69. 3 -Distance Measures.mp449.6 MB
  70. 4 -Distance Measures Use cases.mp474.0 MB
  71. 5 -Use of Distance Measures in Machine Learning.mp423.8 MB
  72. 6 -KMeans Clustering Algorithm.mp426.7 MB
  73. 7 -Example - Clustering the data using KMeans Clustering Algorithm.mp422.4 MB
  74. 8 -KMeans Cost Function.mp410.9 MB
  75. 9 -KMeans Use cases.mp438.3 MB
  76. 1 -tSNE Introduction.mp463.0 MB
  77. 2 -tSNE Algorithm Steps.mp414.3 MB
  78. 3 -tSNE use case.mp423.0 MB
  79. 4 -tSNE Using the MINIST Dataset.mp442.4 MB
  80. 1 -Introduction.mp418.6 MB
  81. 10 -Random Forest.mp463.0 MB
  82. 11 -Hyperparameters to tune Random Forest.mp453.6 MB
  83. 12 -Stacking Ensemble Learning.mp477.1 MB
  84. 13 -Use case On Stacking.mp441.3 MB
  85. 14 -Boosting.mp484.0 MB
  86. 15 -Boosting Algorithm Steps.mp445.5 MB
  87. 16 -AdaBoosting Ensemble Learning Model.mp439.5 MB
  88. 17 -AdaBoosting Ensemble Learning - Example.mp447.9 MB
  89. 18 -Bagging and Boosting Comparison.mp423.7 MB
  90. 19 -Gradient Boosting Algorithm.mp436.3 MB
  91. 2 -What is Ensemble and Model Error.mp448.8 MB
  92. 20 -Gradient Boosting Example.mp423.9 MB
  93. 21 -XGBoost Ensemble Learning Method.mp422.5 MB
  94. 3 -Bias and Variance Tradeoff.mp460.4 MB
  95. 4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp463.3 MB
  96. 5 -Random Sampling with Replacement.mp436.5 MB
  97. 6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp418.7 MB
  98. 7 -Use case 2 - Understanding the 63.21% Rule in Sampling with Replacement.mp440.7 MB
  99. 8 -Bagging.mp416.7 MB
  100. 9 -Vanilla Bagging Algorithm.mp444.0 MB
  101. Bonus Resources.txt70 bytes

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